Conservation agencies around the world are tasked with protecting endangered wildlife from poaching. Despite their substantial efforts, however, species are continuing to be poached to critical status and, in some cases, extinction. In South Africa, rhino poaching has seen a recent escalation in frequency; while only 122 rhinos were poached in 2009, a record 1215 rhinos were poached in 2014 (approximately 1 rhino every eight hours)[the Rhino International, 2015]. To combat poaching, conservation agencies send well-trained rangers to patrol designated protected areas. However, these agencies have limited resources and are unable to provide 100% coverage to the entire area at all times. Thus, it is important that agencies make the most efficient use of their patrolling resources, and we introduce Green Security Games (GSGs) as a tool to aid agencies in designing effective patrols. First introduced by [Von Stengel and Zamir, 2004] as a Leadership Game, Stackelberg Games have been applied in a variety of Security Game research (i.e., Stackelberg Security Games, or SSGs). In particular, the focus on randomization in Stackelberg Games lends itself to solving real-world security problems where defenders have limited resources, such as randomly allocating Federal Air Marshals to international flights [Tsai et al., 2009]. However, the SSG model focuses on generating an optimal defender strategy against a single defender-attacker interaction (e.g., a single terrorist attack). For domains where attacks occur frequently, such as in wildlife conservation, another type of Security Game is needed that effectively models the repeated interactions between the defender and the attacker. While still following the Leader-Follower paradigm of SSGs, GSGs have been developed as a way of applying Game Theory to assist wildlife conservation efforts, whether its to prevent illegal fishing [Haskell et al., 2014], illegal logging [Johnson et al., 2012], or wildlife poaching [Yang et al., 2014]. GSGs are similar to SSGs except that, in GSGs, the game takes place over N rounds. In SSGs, once the attacker makes a decision, the game is over, but in GSGs, the attacker (e.g., the poacher) and defender have multiple rounds in which they can adapt to each other’s choices in previous rounds. This multi-round feature of GSGs introduces some key research challenges that are being studied: (1) how can we incorporate the attacker’s previous choices into our model of their behavior, in order to improve the defender’s strategy, [Yang et al., 2014; Kar et al., 2015] and (2) how do we choose a strategy such that the long-term payoff (i.e., cumulative expected utility) is maximized [Fang et al., 2015]? In addition to exploring these open research questions, we also discuss field tests of the Protection Assistant for Wildlife Security (PAWS) software in Uganda and Malaysia.
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